{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting return from Gold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Gold has been the original store of value and medium of exchange for mankind for centuries till paper/or fiat currency took over a couple of centuries ago. However, most of the sustainable paper currencyies were backed by Gold as late as 1971, when the Bretton Woods agreement was scrapped and world currencies became a true $'Fiat'$ currency.\n",
    "\n",
    "Gold however continues to be os interest not only as metal of choice for jewellery, but also as store of value and often advisable part of investment portfolio as it tends to be a hedge and safe haven when economies tend to (or atleat appear to) be in or at brink of collapse.\n",
    "\n",
    "Currently there are numerous instruments which can give an investor exposure to Gold and they not necessarily need to keep it physically in their vaults. Exchange traded Funds (ETFs) is the most widely used instrument. As of April 2020, a total of **USD175bn** is invested in Gold ETFs across the globe. This was corpus was just **USD24bn in 2008** before the Global Financial Crisis (GFC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the series we will take different approaches to predict return from Gold prices using Machine learning. We will use supervised learning methods of regression and classification. We will then use Time Series methods. And then finally we will try to integrate them to see of their predictive ppowers increases due to integration.\n",
    "\n",
    "First we will go the regression route to predict future returns of Gold over next 2 weeks and 3 weeks period. We will do this by using historical returns of different instruments which I beleive impact or likely to impact the outlook towards Gold. The fundamental reason is, I term Gold as a 'reactionary' asset. It has little fundamentals of its own and movement in prices is often is a derivative of how investors view other asset classes (equities and commdities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing and Preparing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this and subsequent exercises we will need closing price of several instruments for past 10 years . There are various paid (Reuters, Bloomberg) and free resources (IEX, Quandl, Yahoofinance, Google finance) that we can use to either extract and load data in csv or we can directly call their APIs. Since in this project I needed different type of asset classes (Equities, Commodities, Debt and precious metals) I found the 'yahoofinancials' package to be very helpful and straight forward. (https://pypi.org/project/yahoofinancials/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Libraries\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from yahoofinancials import YahooFinancials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have prepared a list instruments for which we need to import data. yahoofinancials package requires Yahoo ticker symbols. The list contains the ticker symbols and their descriptions. The excel file containing the list can be found here..... We import that file and extract the tciker symbols and the names as seprarate lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GC=F</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SI=F</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CL=F</td>\n",
       "      <td>Crude Oil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>^GSPC</td>\n",
       "      <td>S&amp;P500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>^RUT</td>\n",
       "      <td>Russel 2000 Index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ZN=F</td>\n",
       "      <td>10 Yr US T-Note futures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ZT=F</td>\n",
       "      <td>2 Yr US T-Note Futures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PL=F</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HG=F</td>\n",
       "      <td>Copper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DX=F</td>\n",
       "      <td>Dollar Index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>^VIX</td>\n",
       "      <td>Volatility Index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>S=F</td>\n",
       "      <td>Soybean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>EEM</td>\n",
       "      <td>MSCI EM ETF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EURUSD=X</td>\n",
       "      <td>Euro USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>^N100</td>\n",
       "      <td>Euronext100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>^IXIC</td>\n",
       "      <td>Nasdaq</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Ticker              Description\n",
       "0       GC=F                     Gold\n",
       "1       SI=F                   Silver\n",
       "2       CL=F                Crude Oil\n",
       "3      ^GSPC                   S&P500\n",
       "4       ^RUT        Russel 2000 Index\n",
       "5       ZN=F  10 Yr US T-Note futures\n",
       "6       ZT=F   2 Yr US T-Note Futures\n",
       "7       PL=F                 Platinum\n",
       "8       HG=F                   Copper\n",
       "9       DX=F             Dollar Index\n",
       "10      ^VIX         Volatility Index\n",
       "11       S=F                  Soybean\n",
       "12       EEM              MSCI EM ETF\n",
       "13  EURUSD=X                 Euro USD\n",
       "14     ^N100              Euronext100\n",
       "15     ^IXIC                   Nasdaq"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticker_details = pd.read_excel(\"Ticker List.xlsx\")\n",
    "ticker_details.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker = ticker_details['Ticker'].to_list()\n",
    "names = ticker_details['Description'].to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the list, we need to define what date range we need to import the data for. The period I have chosen is Jan 2010 till 1st Mar 2020. The reason I did not pull data prior to that is because the GFC in 2008-09 massively changed the economic and market landscapes. Relationships pririo to that peirod might be of less relevance now. We also dont want to feed very less data as the models might tend to overfit.\n",
    "\n",
    "We create a date-range and write it to an empty dataframe named values where we would extract and past the values we pull from yahoofinancials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extracting Data from Yahoo Finance and Adding them to Values table using date as key\n",
    "end_date= \"2020-03-01\"\n",
    "start_date = \"2010-01-01\"\n",
    "date_range = pd.bdate_range(start=start_date,end=end_date)\n",
    "values = pd.DataFrame({ 'Date': date_range})\n",
    "values['Date']= pd.to_datetime(values['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the date range in dataframe, we need to use ticker symbols to pull out data from the API. yahoofinancials returns the output in a JSON format. The following code loops over the the list of ticker symbols and extracts just the closing prices for all the historical dates and keeps them adding to the dataframe horizontally. Note I have used the merge function to mantain the sanctity of dates. Given these asset classes might have different regional and trading holidays, the date ranges are not bound to be the same. By merging, we will eventually have several NAs which we will frontfill later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2651, 17)\n",
      "Date                         0\n",
      "Gold                       117\n",
      "Silver                     117\n",
      "Crude Oil                  116\n",
      "S&P500                      95\n",
      "Russel 2000 Index           95\n",
      "10 Yr US T-Note futures    118\n",
      "2 Yr US T-Note Futures     116\n",
      "Platinum                   117\n",
      "Copper                     117\n",
      "Dollar Index               121\n",
      "Volatility Index            95\n",
      "Soybean                    116\n",
      "MSCI EM ETF                 95\n",
      "Euro USD                   311\n",
      "Euronext100                 55\n",
      "Nasdaq                      95\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Crude Oil</th>\n",
       "      <th>S&amp;P500</th>\n",
       "      <th>Russel 2000 Index</th>\n",
       "      <th>10 Yr US T-Note futures</th>\n",
       "      <th>2 Yr US T-Note Futures</th>\n",
       "      <th>Platinum</th>\n",
       "      <th>Copper</th>\n",
       "      <th>Dollar Index</th>\n",
       "      <th>Volatility Index</th>\n",
       "      <th>Soybean</th>\n",
       "      <th>MSCI EM ETF</th>\n",
       "      <th>Euro USD</th>\n",
       "      <th>Euronext100</th>\n",
       "      <th>Nasdaq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2646</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>1672.400024</td>\n",
       "      <td>18.868000</td>\n",
       "      <td>51.430000</td>\n",
       "      <td>3225.889893</td>\n",
       "      <td>1628.099976</td>\n",
       "      <td>132.656006</td>\n",
       "      <td>108.273003</td>\n",
       "      <td>971.700012</td>\n",
       "      <td>2.5925</td>\n",
       "      <td>99.283997</td>\n",
       "      <td>25.030001</td>\n",
       "      <td>874.25</td>\n",
       "      <td>41.669998</td>\n",
       "      <td>1.083905</td>\n",
       "      <td>1120.449951</td>\n",
       "      <td>9221.280273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2647</th>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>1646.900024</td>\n",
       "      <td>18.183001</td>\n",
       "      <td>49.900002</td>\n",
       "      <td>3128.209961</td>\n",
       "      <td>1571.900024</td>\n",
       "      <td>133.125000</td>\n",
       "      <td>108.398003</td>\n",
       "      <td>929.799988</td>\n",
       "      <td>2.5965</td>\n",
       "      <td>98.901001</td>\n",
       "      <td>27.850000</td>\n",
       "      <td>879.00</td>\n",
       "      <td>41.340000</td>\n",
       "      <td>1.084920</td>\n",
       "      <td>1099.270020</td>\n",
       "      <td>8965.610352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>1640.000000</td>\n",
       "      <td>17.826000</td>\n",
       "      <td>48.730000</td>\n",
       "      <td>3116.389893</td>\n",
       "      <td>1552.760010</td>\n",
       "      <td>133.328003</td>\n",
       "      <td>108.480003</td>\n",
       "      <td>912.299988</td>\n",
       "      <td>2.5765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.559999</td>\n",
       "      <td>881.00</td>\n",
       "      <td>41.669998</td>\n",
       "      <td>1.088200</td>\n",
       "      <td>1099.410034</td>\n",
       "      <td>8980.780273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2649</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>1643.099976</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>46.810001</td>\n",
       "      <td>2978.760010</td>\n",
       "      <td>1497.869995</td>\n",
       "      <td>133.218994</td>\n",
       "      <td>108.698997</td>\n",
       "      <td>904.299988</td>\n",
       "      <td>2.5690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.160000</td>\n",
       "      <td>895.00</td>\n",
       "      <td>40.669998</td>\n",
       "      <td>1.088589</td>\n",
       "      <td>1059.359985</td>\n",
       "      <td>8566.480469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>1642.500000</td>\n",
       "      <td>17.658001</td>\n",
       "      <td>47.090000</td>\n",
       "      <td>2954.219971</td>\n",
       "      <td>1476.430054</td>\n",
       "      <td>133.328003</td>\n",
       "      <td>108.773003</td>\n",
       "      <td>905.500000</td>\n",
       "      <td>2.5715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.110001</td>\n",
       "      <td>891.75</td>\n",
       "      <td>40.520000</td>\n",
       "      <td>1.099723</td>\n",
       "      <td>1021.979980</td>\n",
       "      <td>8567.370117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date         Gold     Silver  Crude Oil       S&P500  \\\n",
       "2646 2020-02-24  1672.400024  18.868000  51.430000  3225.889893   \n",
       "2647 2020-02-25  1646.900024  18.183001  49.900002  3128.209961   \n",
       "2648 2020-02-26  1640.000000  17.826000  48.730000  3116.389893   \n",
       "2649 2020-02-27  1643.099976  17.799999  46.810001  2978.760010   \n",
       "2650 2020-02-28  1642.500000  17.658001  47.090000  2954.219971   \n",
       "\n",
       "      Russel 2000 Index  10 Yr US T-Note futures  2 Yr US T-Note Futures  \\\n",
       "2646        1628.099976               132.656006              108.273003   \n",
       "2647        1571.900024               133.125000              108.398003   \n",
       "2648        1552.760010               133.328003              108.480003   \n",
       "2649        1497.869995               133.218994              108.698997   \n",
       "2650        1476.430054               133.328003              108.773003   \n",
       "\n",
       "        Platinum  Copper  Dollar Index  Volatility Index  Soybean  \\\n",
       "2646  971.700012  2.5925     99.283997         25.030001   874.25   \n",
       "2647  929.799988  2.5965     98.901001         27.850000   879.00   \n",
       "2648  912.299988  2.5765           NaN         27.559999   881.00   \n",
       "2649  904.299988  2.5690           NaN         39.160000   895.00   \n",
       "2650  905.500000  2.5715           NaN         40.110001   891.75   \n",
       "\n",
       "      MSCI EM ETF  Euro USD  Euronext100       Nasdaq  \n",
       "2646    41.669998  1.083905  1120.449951  9221.280273  \n",
       "2647    41.340000  1.084920  1099.270020  8965.610352  \n",
       "2648    41.669998  1.088200  1099.410034  8980.780273  \n",
       "2649    40.669998  1.088589  1059.359985  8566.480469  \n",
       "2650    40.520000  1.099723  1021.979980  8567.370117  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extracting Data from Yahoo Finance and Adding them to Values table using date as key\n",
    "for i in ticker:\n",
    "    raw_data = YahooFinancials(i)\n",
    "    raw_data = raw_data.get_historical_price_data(start_date, end_date, \"daily\")\n",
    "    df = pd.DataFrame(raw_data[i]['prices'])[['formatted_date','adjclose']]\n",
    "    df.columns = ['Date1',i]\n",
    "    df['Date1']= pd.to_datetime(df['Date1'])\n",
    "    values = values.merge(df,how='left',left_on='Date',right_on='Date1')\n",
    "    values = values.drop(labels='Date1',axis=1)\n",
    "\n",
    "#Renaming columns to represent instrument names rather than their ticker codes for ease of readability\n",
    "names.insert(0,'Date')\n",
    "values.columns = names\n",
    "print(values.shape)\n",
    "print(values.isna().sum())\n",
    "values.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                       0\n",
       "Gold                       0\n",
       "Silver                     0\n",
       "Crude Oil                  0\n",
       "S&P500                     0\n",
       "Russel 2000 Index          0\n",
       "10 Yr US T-Note futures    0\n",
       "2 Yr US T-Note Futures     0\n",
       "Platinum                   0\n",
       "Copper                     0\n",
       "Dollar Index               0\n",
       "Volatility Index           0\n",
       "Soybean                    0\n",
       "MSCI EM ETF                0\n",
       "Euro USD                   0\n",
       "Euronext100                0\n",
       "Nasdaq                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Front filling the NaN values in the data set\n",
    "values = values.fillna(method=\"ffill\",axis=0)\n",
    "values = values.fillna(method=\"bfill\",axis=0)\n",
    "values.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Crude Oil</th>\n",
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       "      <th>Platinum</th>\n",
       "      <th>Copper</th>\n",
       "      <th>Dollar Index</th>\n",
       "      <th>Volatility Index</th>\n",
       "      <th>Soybean</th>\n",
       "      <th>MSCI EM ETF</th>\n",
       "      <th>Euro USD</th>\n",
       "      <th>Euronext100</th>\n",
       "      <th>Nasdaq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2646</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>1672.4</td>\n",
       "      <td>18.9</td>\n",
       "      <td>51.4</td>\n",
       "      <td>3225.9</td>\n",
       "      <td>1628.1</td>\n",
       "      <td>132.7</td>\n",
       "      <td>108.3</td>\n",
       "      <td>971.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>99.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>874.2</td>\n",
       "      <td>41.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1120.4</td>\n",
       "      <td>9221.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2647</th>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>1646.9</td>\n",
       "      <td>18.2</td>\n",
       "      <td>49.9</td>\n",
       "      <td>3128.2</td>\n",
       "      <td>1571.9</td>\n",
       "      <td>133.1</td>\n",
       "      <td>108.4</td>\n",
       "      <td>929.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>98.9</td>\n",
       "      <td>27.9</td>\n",
       "      <td>879.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1099.3</td>\n",
       "      <td>8965.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>48.7</td>\n",
       "      <td>3116.4</td>\n",
       "      <td>1552.8</td>\n",
       "      <td>133.3</td>\n",
       "      <td>108.5</td>\n",
       "      <td>912.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>98.9</td>\n",
       "      <td>27.6</td>\n",
       "      <td>881.0</td>\n",
       "      <td>41.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1099.4</td>\n",
       "      <td>8980.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2649</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>1643.1</td>\n",
       "      <td>17.8</td>\n",
       "      <td>46.8</td>\n",
       "      <td>2978.8</td>\n",
       "      <td>1497.9</td>\n",
       "      <td>133.2</td>\n",
       "      <td>108.7</td>\n",
       "      <td>904.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>98.9</td>\n",
       "      <td>39.2</td>\n",
       "      <td>895.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1059.4</td>\n",
       "      <td>8566.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>1642.5</td>\n",
       "      <td>17.7</td>\n",
       "      <td>47.1</td>\n",
       "      <td>2954.2</td>\n",
       "      <td>1476.4</td>\n",
       "      <td>133.3</td>\n",
       "      <td>108.8</td>\n",
       "      <td>905.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>98.9</td>\n",
       "      <td>40.1</td>\n",
       "      <td>891.8</td>\n",
       "      <td>40.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>8567.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date    Gold  Silver  Crude Oil  S&P500  Russel 2000 Index  \\\n",
       "2646 2020-02-24  1672.4    18.9       51.4  3225.9             1628.1   \n",
       "2647 2020-02-25  1646.9    18.2       49.9  3128.2             1571.9   \n",
       "2648 2020-02-26  1640.0    17.8       48.7  3116.4             1552.8   \n",
       "2649 2020-02-27  1643.1    17.8       46.8  2978.8             1497.9   \n",
       "2650 2020-02-28  1642.5    17.7       47.1  2954.2             1476.4   \n",
       "\n",
       "      10 Yr US T-Note futures  2 Yr US T-Note Futures  Platinum  Copper  \\\n",
       "2646                    132.7                   108.3     971.7     2.6   \n",
       "2647                    133.1                   108.4     929.8     2.6   \n",
       "2648                    133.3                   108.5     912.3     2.6   \n",
       "2649                    133.2                   108.7     904.3     2.6   \n",
       "2650                    133.3                   108.8     905.5     2.6   \n",
       "\n",
       "      Dollar Index  Volatility Index  Soybean  MSCI EM ETF  Euro USD  \\\n",
       "2646          99.3              25.0    874.2         41.7       1.1   \n",
       "2647          98.9              27.9    879.0         41.3       1.1   \n",
       "2648          98.9              27.6    881.0         41.7       1.1   \n",
       "2649          98.9              39.2    895.0         40.7       1.1   \n",
       "2650          98.9              40.1    891.8         40.5       1.1   \n",
       "\n",
       "      Euronext100  Nasdaq  \n",
       "2646       1120.4  9221.3  \n",
       "2647       1099.3  8965.6  \n",
       "2648       1099.4  8980.8  \n",
       "2649       1059.4  8566.5  \n",
       "2650       1022.0  8567.4  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Co-ercing numeric type to all columns except Date\n",
    "cols=values.columns.drop('Date')\n",
    "values[cols] = values[cols].apply(pd.to_numeric,errors='coerce').round(decimals=1)\n",
    "values.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "values.to_csv(\"Training Data_Values.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In approach we highlighted that we will used lagged returns of the listed instruments to predict future returns on Gold. Here we go on to calculate shortterm returns of all the instruments and longer term returns of few selected instruments. The fundamental idea behind it is, that if a certain asset has highly outperformed or underperformed, there is greater likelihood of portfolio rebalancing which would impact returns on other asset clasees. Eg: If the stock markets (S&P500) has shown stupendous returns in past 6 months, asset managers might want to book profits and allocate some funds to say precious metals and prepare for stock market correction. We will however, use Machine Learning to evaluate the hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2651, 1)\n",
      "(2651, 17)\n",
      "(2651, 33)\n",
      "(2651, 49)\n",
      "(2651, 65)\n",
      "(2651, 81)\n",
      "(2651, 81)\n",
      "(2651, 86)\n",
      "(2651, 91)\n",
      "(2651, 96)\n",
      "(2651, 101)\n"
     ]
    }
   ],
   "source": [
    "imp = ['Gold','Silver', 'Crude Oil', 'S&P500','MSCI EM ETF']\n",
    "# Calculating Short term -Historical Returns\n",
    "change_days = [1,3,5,14,21]\n",
    "\n",
    "data = pd.DataFrame(data=values['Date'])\n",
    "for i in change_days:\n",
    "    print(data.shape)\n",
    "    x= values[cols].pct_change(periods=i).add_suffix(\"-T-\"+str(i))\n",
    "    data=pd.concat(objs=(data,x),axis=1)\n",
    "    x=[]\n",
    "print(data.shape)\n",
    "\n",
    "# Calculating Long term Historical Returns\n",
    "change_days = [60,90,180,250]\n",
    "\n",
    "for i in change_days:\n",
    "    print(data.shape)\n",
    "    x= values[imp].pct_change(periods=i).add_suffix(\"-T-\"+str(i))\n",
    "    data=pd.concat(objs=(data,x),axis=1)\n",
    "    x=[]\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides just the lagged returns, we also see how far the current Gold price is from its moving average for with different window. This is a very commonly used metric in technical analysis where moving averages offer supports and resistances for asset prices. We use a combination of simple and exponential moving averages. We then add these moving averages to the existing feature space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2472, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Gold/15SMA</th>\n",
       "      <th>Gold/30SMA</th>\n",
       "      <th>Gold/60SMA</th>\n",
       "      <th>Gold/90SMA</th>\n",
       "      <th>Gold/180SMA</th>\n",
       "      <th>Gold/90EMA</th>\n",
       "      <th>Gold/180EMA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>2010-09-09</td>\n",
       "      <td>0.005005</td>\n",
       "      <td>0.020246</td>\n",
       "      <td>0.026862</td>\n",
       "      <td>0.026428</td>\n",
       "      <td>0.067496</td>\n",
       "      <td>0.032506</td>\n",
       "      <td>0.046162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2010-09-10</td>\n",
       "      <td>0.000536</td>\n",
       "      <td>0.014916</td>\n",
       "      <td>0.023422</td>\n",
       "      <td>0.022489</td>\n",
       "      <td>0.063095</td>\n",
       "      <td>0.028204</td>\n",
       "      <td>0.041912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2010-09-13</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.013705</td>\n",
       "      <td>0.023840</td>\n",
       "      <td>0.022565</td>\n",
       "      <td>0.062965</td>\n",
       "      <td>0.028040</td>\n",
       "      <td>0.041852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2010-09-14</td>\n",
       "      <td>0.017736</td>\n",
       "      <td>0.031368</td>\n",
       "      <td>0.043642</td>\n",
       "      <td>0.042295</td>\n",
       "      <td>0.083187</td>\n",
       "      <td>0.047219</td>\n",
       "      <td>0.061593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2010-09-15</td>\n",
       "      <td>0.013857</td>\n",
       "      <td>0.026901</td>\n",
       "      <td>0.040711</td>\n",
       "      <td>0.039604</td>\n",
       "      <td>0.079958</td>\n",
       "      <td>0.043701</td>\n",
       "      <td>0.058291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Gold/15SMA  Gold/30SMA  Gold/60SMA  Gold/90SMA  Gold/180SMA  \\\n",
       "179 2010-09-09    0.005005    0.020246    0.026862    0.026428     0.067496   \n",
       "180 2010-09-10    0.000536    0.014916    0.023422    0.022489     0.063095   \n",
       "181 2010-09-13    0.000043    0.013705    0.023840    0.022565     0.062965   \n",
       "182 2010-09-14    0.017736    0.031368    0.043642    0.042295     0.083187   \n",
       "183 2010-09-15    0.013857    0.026901    0.040711    0.039604     0.079958   \n",
       "\n",
       "     Gold/90EMA  Gold/180EMA  \n",
       "179    0.032506     0.046162  \n",
       "180    0.028204     0.041912  \n",
       "181    0.028040     0.041852  \n",
       "182    0.047219     0.061593  \n",
       "183    0.043701     0.058291  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating Moving averages for Gold\n",
    "moving_avg = pd.DataFrame(values['Date'],columns=['Date'])\n",
    "moving_avg['Date']=pd.to_datetime(moving_avg['Date'],format='%Y-%b-%d')\n",
    "moving_avg['Gold/15SMA'] = (values['Gold']/(values['Gold'].rolling(window=15).mean()))-1\n",
    "moving_avg['Gold/30SMA'] = (values['Gold']/(values['Gold'].rolling(window=30).mean()))-1\n",
    "moving_avg['Gold/60SMA'] = (values['Gold']/(values['Gold'].rolling(window=60).mean()))-1\n",
    "moving_avg['Gold/90SMA'] = (values['Gold']/(values['Gold'].rolling(window=90).mean()))-1\n",
    "moving_avg['Gold/180SMA'] = (values['Gold']/(values['Gold'].rolling(window=180).mean()))-1\n",
    "moving_avg['Gold/90EMA'] = (values['Gold']/(values['Gold'].ewm(span=90,adjust=True,ignore_na=True).mean()))-1\n",
    "moving_avg['Gold/180EMA'] = (values['Gold']/(values['Gold'].ewm(span=180,adjust=True,ignore_na=True).mean()))-1\n",
    "moving_avg = moving_avg.dropna(axis=0)\n",
    "print(moving_avg.shape)\n",
    "moving_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2651, 101)\n",
      "(2651, 108)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Date               0\n",
       "Gold-T-1           1\n",
       "Silver-T-1         1\n",
       "Crude Oil-T-1      1\n",
       "S&P500-T-1         1\n",
       "                ... \n",
       "Gold/60SMA       179\n",
       "Gold/90SMA       179\n",
       "Gold/180SMA      179\n",
       "Gold/90EMA       179\n",
       "Gold/180EMA      179\n",
       "Length: 108, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging Moving Average values to the feature space\n",
    "print(data.shape)\n",
    "data['Date']=pd.to_datetime(data['Date'],format='%Y-%b-%d')\n",
    "data = pd.merge(left=data,right=moving_avg,how='left',on='Date')\n",
    "print(data.shape)\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This wall all about features. Now we need to create targets, i.e what we want to predict. Since we are predicting returns, we need to pick a horizon for which we need to predict returns. I have chosen 14-day and 22-day horizons because other smaller horizons tend to be very volatile and lack and predictive power. One can however, experiment with other horizons as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2651, 1)\n",
      "(2651, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Date          0\n",
       "Gold-T+14    14\n",
       "Gold-T+22    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Caluculating forward returns for Target\n",
    "y = pd.DataFrame(data=values['Date'])\n",
    "print(y.shape)\n",
    "y['Gold-T+14']=values[\"Gold\"].pct_change(periods=-14)\n",
    "y['Gold-T+22']=values[\"Gold\"].pct_change(periods=-22)\n",
    "print(y.shape)\n",
    "y.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2651, 108)\n",
      "(2401, 108)\n",
      "(2629, 3)\n"
     ]
    }
   ],
   "source": [
    "# Removing NAs\n",
    "print(data.shape)\n",
    "data = data[data['Gold-T-250'].notna()]\n",
    "y = y[y['Gold-T+22'].notna()]\n",
    "print(data.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will merge the Target variables with the feature space to get a data whcih we can finally start modelling on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2379, 110)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Date             0\n",
       "Gold-T-1         0\n",
       "Silver-T-1       0\n",
       "Crude Oil-T-1    0\n",
       "S&P500-T-1       0\n",
       "                ..\n",
       "Gold/180SMA      0\n",
       "Gold/90EMA       0\n",
       "Gold/180EMA      0\n",
       "Gold-T+14        0\n",
       "Gold-T+22        0\n",
       "Length: 110, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding Target Variables\n",
    "data = pd.merge(left=data,right=y,how='inner',on='Date',suffixes=(False,False))\n",
    "print(data.shape)\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"Training Data.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = data.corr().iloc[:,-2:].drop(labels=['Gold-T+14','Gold-T+22'],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15bdfc12f98>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(corr.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "corr_data = data.tail(2000).corr()\n",
    "corr_data = pd.DataFrame(corr_data['Gold-T+14'])\n",
    "#corr_data = corr_data.iloc[3:,]\n",
    "corr_data = corr_data.sort_values('Gold-T+14',ascending=False)\n",
    "#corr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15be1cf7e80>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(corr_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 22 Day Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data = pd.read_csv(\"Training Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.regression import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Gold-T-1</th>\n",
       "      <th>Silver-T-1</th>\n",
       "      <th>Crude Oil-T-1</th>\n",
       "      <th>S&amp;P500-T-1</th>\n",
       "      <th>Russel 2000 Index-T-1</th>\n",
       "      <th>10 Yr US T-Note futures-T-1</th>\n",
       "      <th>2 Yr US T-Note Futures-T-1</th>\n",
       "      <th>Platinum-T-1</th>\n",
       "      <th>Copper-T-1</th>\n",
       "      <th>...</th>\n",
       "      <th>S&amp;P500-T-250</th>\n",
       "      <th>MSCI EM ETF-T-250</th>\n",
       "      <th>Gold/15SMA</th>\n",
       "      <th>Gold/30SMA</th>\n",
       "      <th>Gold/60SMA</th>\n",
       "      <th>Gold/90SMA</th>\n",
       "      <th>Gold/180SMA</th>\n",
       "      <th>Gold/90EMA</th>\n",
       "      <th>Gold/180EMA</th>\n",
       "      <th>Gold-T+22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-12-17</td>\n",
       "      <td>0.005984</td>\n",
       "      <td>0.010417</td>\n",
       "      <td>0.003421</td>\n",
       "      <td>0.000805</td>\n",
       "      <td>0.003734</td>\n",
       "      <td>0.008306</td>\n",
       "      <td>0.000913</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>0.02439</td>\n",
       "      <td>...</td>\n",
       "      <td>0.097882</td>\n",
       "      <td>0.092219</td>\n",
       "      <td>-0.008206</td>\n",
       "      <td>-0.001805</td>\n",
       "      <td>0.012638</td>\n",
       "      <td>0.040506</td>\n",
       "      <td>0.092005</td>\n",
       "      <td>0.035494</td>\n",
       "      <td>0.070472</td>\n",
       "      <td>0.007675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-12-20</td>\n",
       "      <td>0.005005</td>\n",
       "      <td>0.006873</td>\n",
       "      <td>0.009091</td>\n",
       "      <td>0.002573</td>\n",
       "      <td>0.003592</td>\n",
       "      <td>-0.000824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007183</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100706</td>\n",
       "      <td>0.086455</td>\n",
       "      <td>-0.004174</td>\n",
       "      <td>0.003610</td>\n",
       "      <td>0.016601</td>\n",
       "      <td>0.044304</td>\n",
       "      <td>0.096390</td>\n",
       "      <td>0.039744</td>\n",
       "      <td>0.074871</td>\n",
       "      <td>0.011166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-12-21</td>\n",
       "      <td>0.001949</td>\n",
       "      <td>0.003413</td>\n",
       "      <td>0.011261</td>\n",
       "      <td>0.006014</td>\n",
       "      <td>0.010482</td>\n",
       "      <td>-0.000824</td>\n",
       "      <td>-0.000912</td>\n",
       "      <td>0.006547</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103916</td>\n",
       "      <td>0.097421</td>\n",
       "      <td>-0.002386</td>\n",
       "      <td>0.006090</td>\n",
       "      <td>0.017567</td>\n",
       "      <td>0.044925</td>\n",
       "      <td>0.097391</td>\n",
       "      <td>0.040811</td>\n",
       "      <td>0.075992</td>\n",
       "      <td>0.030969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-12-22</td>\n",
       "      <td>-0.001009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007795</td>\n",
       "      <td>0.003348</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>-0.009901</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005227</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107027</td>\n",
       "      <td>0.097143</td>\n",
       "      <td>-0.003368</td>\n",
       "      <td>0.005374</td>\n",
       "      <td>0.015569</td>\n",
       "      <td>0.042501</td>\n",
       "      <td>0.095189</td>\n",
       "      <td>0.038850</td>\n",
       "      <td>0.073962</td>\n",
       "      <td>0.034154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-12-23</td>\n",
       "      <td>-0.004903</td>\n",
       "      <td>-0.003401</td>\n",
       "      <td>0.011050</td>\n",
       "      <td>-0.001589</td>\n",
       "      <td>-0.002024</td>\n",
       "      <td>-0.003333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.004506</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100815</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>-0.007851</td>\n",
       "      <td>0.001003</td>\n",
       "      <td>0.009699</td>\n",
       "      <td>0.036124</td>\n",
       "      <td>0.088766</td>\n",
       "      <td>0.032987</td>\n",
       "      <td>0.067834</td>\n",
       "      <td>0.026404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 109 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Gold-T-1  Silver-T-1  Crude Oil-T-1  S&P500-T-1  \\\n",
       "0 2010-12-17  0.005984    0.010417       0.003421    0.000805   \n",
       "1 2010-12-20  0.005005    0.006873       0.009091    0.002573   \n",
       "2 2010-12-21  0.001949    0.003413       0.011261    0.006014   \n",
       "3 2010-12-22 -0.001009    0.000000       0.007795    0.003348   \n",
       "4 2010-12-23 -0.004903   -0.003401       0.011050   -0.001589   \n",
       "\n",
       "   Russel 2000 Index-T-1  10 Yr US T-Note futures-T-1  \\\n",
       "0               0.003734                     0.008306   \n",
       "1               0.003592                    -0.000824   \n",
       "2               0.010482                    -0.000824   \n",
       "3               0.000127                    -0.009901   \n",
       "4              -0.002024                    -0.003333   \n",
       "\n",
       "   2 Yr US T-Note Futures-T-1  Platinum-T-1  Copper-T-1  ...  S&P500-T-250  \\\n",
       "0                    0.000913      0.001415     0.02439  ...      0.097882   \n",
       "1                    0.000000      0.007183     0.00000  ...      0.100706   \n",
       "2                   -0.000912      0.006547     0.02381  ...      0.103916   \n",
       "3                    0.000000      0.005227     0.00000  ...      0.107027   \n",
       "4                    0.000000     -0.004506     0.00000  ...      0.100815   \n",
       "\n",
       "   MSCI EM ETF-T-250  Gold/15SMA  Gold/30SMA  Gold/60SMA  Gold/90SMA  \\\n",
       "0           0.092219   -0.008206   -0.001805    0.012638    0.040506   \n",
       "1           0.086455   -0.004174    0.003610    0.016601    0.044304   \n",
       "2           0.097421   -0.002386    0.006090    0.017567    0.044925   \n",
       "3           0.097143   -0.003368    0.005374    0.015569    0.042501   \n",
       "4           0.103448   -0.007851    0.001003    0.009699    0.036124   \n",
       "\n",
       "   Gold/180SMA  Gold/90EMA  Gold/180EMA  Gold-T+22  \n",
       "0     0.092005    0.035494     0.070472   0.007675  \n",
       "1     0.096390    0.039744     0.074871   0.011166  \n",
       "2     0.097391    0.040811     0.075992   0.030969  \n",
       "3     0.095189    0.038850     0.073962   0.034154  \n",
       "4     0.088766    0.032987     0.067834   0.026404  \n",
       "\n",
       "[5 rows x 109 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_22= data.drop(['Gold-T+14'],axis=1)\n",
    "data_22.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442be\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow0_col1\" class=\"data row0 col1\" >11</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow3_col1\" class=\"data row3 col1\" >(2379, 109)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow5_col1\" class=\"data row5 col1\" >107</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow6_col1\" class=\"data row6 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow10_col1\" class=\"data row10 col1\" >(2379, 109)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow11_col1\" class=\"data row11 col1\" >(1665, 107)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow12_col1\" class=\"data row12 col1\" >(714, 107)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442belevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_b0f5bb82_82bf_11ea_8aaa_9cb6d09442berow41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15be79d3198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=setup(data_22,target='Gold-T+22',\n",
    "        ignore_features=['Date'],session_id=11,\n",
    "        silent=True,profile=False,remove_outliers=False);\n",
    "        #transformation=True,\n",
    "        #pca=True,pca_method='kernel',\n",
    "        #pca_components=10,\n",
    "        #create_clusters=True,\n",
    "        #cluster_iter=10,\n",
    "        #feature_ratio=True,\n",
    "        #normalize=True,\n",
    "        #transform_target=True,\n",
    "       #silent=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
       "        }    #T_6bf0506c_82c0_11ea_9330_9cb6d09442berow16_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_6bf0506c_82c0_11ea_9330_9cb6d09442berow17_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_6bf0506c_82c0_11ea_9330_9cb6d09442berow18_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_6bf0506c_82c0_11ea_9330_9cb6d09442berow18_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
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       "            : ;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
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       "            text-align:  left;\n",
       "        }    #T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col6 {\n",
       "            background-color:  yellow;\n",
       "            text-align:  left;\n",
       "        }</style><table id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442be\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow0_col0\" class=\"data row0 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow0_col1\" class=\"data row0 col1\" >0.0123</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow0_col2\" class=\"data row0 col2\" >0.0003</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow0_col3\" class=\"data row0 col3\" >0.0166</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow0_col4\" class=\"data row0 col4\" >0.8566</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow0_col5\" class=\"data row0 col5\" >0.0152</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow0_col6\" class=\"data row0 col6\" >-0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow1_col0\" class=\"data row1 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow1_col1\" class=\"data row1 col1\" >0.0133</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow1_col2\" class=\"data row1 col2\" >0.0003</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow1_col3\" class=\"data row1 col3\" >0.0176</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow1_col4\" class=\"data row1 col4\" >0.839</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow1_col5\" class=\"data row1 col5\" >0.0161</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow1_col6\" class=\"data row1 col6\" >0.0062</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow2_col0\" class=\"data row2 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow2_col1\" class=\"data row2 col1\" >0.0136</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow2_col2\" class=\"data row2 col2\" >0.0003</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow2_col3\" class=\"data row2 col3\" >0.0182</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow2_col4\" class=\"data row2 col4\" >0.8293</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow2_col5\" class=\"data row2 col5\" >0.0163</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow2_col6\" class=\"data row2 col6\" >-0.0619</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow3_col0\" class=\"data row3 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow3_col1\" class=\"data row3 col1\" >0.0132</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow3_col2\" class=\"data row3 col2\" >0.0003</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow3_col3\" class=\"data row3 col3\" >0.0181</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow3_col4\" class=\"data row3 col4\" >0.8287</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow3_col5\" class=\"data row3 col5\" >0.0154</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow3_col6\" class=\"data row3 col6\" >-0.0419</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow4_col0\" class=\"data row4 col0\" >Random Forest</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow4_col1\" class=\"data row4 col1\" >0.0153</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow4_col2\" class=\"data row4 col2\" >0.0004</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow4_col3\" class=\"data row4 col3\" >0.0207</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow4_col4\" class=\"data row4 col4\" >0.7773</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow4_col5\" class=\"data row4 col5\" >0.0188</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow4_col6\" class=\"data row4 col6\" >-0.0707</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow5_col0\" class=\"data row5 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow5_col1\" class=\"data row5 col1\" >0.0202</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow5_col2\" class=\"data row5 col2\" >0.0007</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow5_col3\" class=\"data row5 col3\" >0.0264</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow5_col4\" class=\"data row5 col4\" >0.6389</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow5_col5\" class=\"data row5 col5\" >0.0239</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow5_col6\" class=\"data row5 col6\" >0.0169</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow6_col0\" class=\"data row6 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow6_col1\" class=\"data row6 col1\" >0.0203</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow6_col2\" class=\"data row6 col2\" >0.0007</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow6_col3\" class=\"data row6 col3\" >0.0266</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow6_col4\" class=\"data row6 col4\" >0.632</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow6_col5\" class=\"data row6 col5\" >0.0243</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow6_col6\" class=\"data row6 col6\" >-0.037</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow7_col0\" class=\"data row7 col0\" >Decision Tree</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow7_col1\" class=\"data row7 col1\" >0.0216</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow7_col2\" class=\"data row7 col2\" >0.0011</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow7_col3\" class=\"data row7 col3\" >0.0327</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow7_col4\" class=\"data row7 col4\" >0.4396</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow7_col5\" class=\"data row7 col5\" >0.0238</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow7_col6\" class=\"data row7 col6\" >0.2117</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow8_col0\" class=\"data row8 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow8_col1\" class=\"data row8 col1\" >0.0296</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow8_col2\" class=\"data row8 col2\" >0.0014</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow8_col3\" class=\"data row8 col3\" >0.037</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow8_col4\" class=\"data row8 col4\" >0.2858</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow8_col5\" class=\"data row8 col5\" >0.0327</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow8_col6\" class=\"data row8 col6\" >0.0721</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow9_col0\" class=\"data row9 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow9_col1\" class=\"data row9 col1\" >0.0302</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow9_col2\" class=\"data row9 col2\" >0.0016</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow9_col3\" class=\"data row9 col3\" >0.0399</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow9_col4\" class=\"data row9 col4\" >0.1723</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow9_col5\" class=\"data row9 col5\" >0.0331</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow9_col6\" class=\"data row9 col6\" >-0.1529</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow10_col0\" class=\"data row10 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow10_col1\" class=\"data row10 col1\" >0.0306</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow10_col2\" class=\"data row10 col2\" >0.0016</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow10_col3\" class=\"data row10 col3\" >0.0401</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow10_col4\" class=\"data row10 col4\" >0.1618</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow10_col5\" class=\"data row10 col5\" >0.0313</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow10_col6\" class=\"data row10 col6\" >-0.0686</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow11_col0\" class=\"data row11 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow11_col1\" class=\"data row11 col1\" >0.0306</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow11_col2\" class=\"data row11 col2\" >0.0016</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow11_col3\" class=\"data row11 col3\" >0.0405</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow11_col4\" class=\"data row11 col4\" >0.1521</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow11_col5\" class=\"data row11 col5\" >0.034</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow11_col6\" class=\"data row11 col6\" >-0.1266</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow12_col0\" class=\"data row12 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow12_col1\" class=\"data row12 col1\" >0.0295</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow12_col2\" class=\"data row12 col2\" >0.0017</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow12_col3\" class=\"data row12 col3\" >0.0408</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow12_col4\" class=\"data row12 col4\" >0.1355</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow12_col5\" class=\"data row12 col5\" >0.0301</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow12_col6\" class=\"data row12 col6\" >-0.1316</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow13_col0\" class=\"data row13 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow13_col1\" class=\"data row13 col1\" >0.0318</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow13_col2\" class=\"data row13 col2\" >0.0018</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow13_col3\" class=\"data row13 col3\" >0.042</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow13_col4\" class=\"data row13 col4\" >0.0853</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow13_col5\" class=\"data row13 col5\" >0.0335</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow13_col6\" class=\"data row13 col6\" >-0.1543</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow14_col0\" class=\"data row14 col0\" >Random Sample Consensus</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow14_col1\" class=\"data row14 col1\" >0.0311</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow14_col2\" class=\"data row14 col2\" >0.0018</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow14_col3\" class=\"data row14 col3\" >0.0422</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow14_col4\" class=\"data row14 col4\" >0.069</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow14_col5\" class=\"data row14 col5\" >0.0304</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow14_col6\" class=\"data row14 col6\" >0.0674</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow15_col0\" class=\"data row15 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow15_col1\" class=\"data row15 col1\" >0.0342</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow15_col2\" class=\"data row15 col2\" >0.002</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow15_col3\" class=\"data row15 col3\" >0.0441</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow15_col4\" class=\"data row15 col4\" >-0.0052</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow15_col5\" class=\"data row15 col5\" >0.0423</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow15_col6\" class=\"data row15 col6\" >0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow16_col0\" class=\"data row16 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow16_col1\" class=\"data row16 col1\" >0.0342</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow16_col2\" class=\"data row16 col2\" >0.002</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow16_col3\" class=\"data row16 col3\" >0.0441</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow16_col4\" class=\"data row16 col4\" >-0.0052</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow16_col5\" class=\"data row16 col5\" >0.0423</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow16_col6\" class=\"data row16 col6\" >0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow17_col0\" class=\"data row17 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow17_col1\" class=\"data row17 col1\" >0.0342</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow17_col2\" class=\"data row17 col2\" >0.002</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow17_col3\" class=\"data row17 col3\" >0.0441</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow17_col4\" class=\"data row17 col4\" >-0.0052</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow17_col5\" class=\"data row17 col5\" >0.0423</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow17_col6\" class=\"data row17 col6\" >0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow18_col0\" class=\"data row18 col0\" >Support Vector Machine</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow18_col1\" class=\"data row18 col1\" >0.0349</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow18_col2\" class=\"data row18 col2\" >0.002</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow18_col3\" class=\"data row18 col3\" >0.0441</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow18_col4\" class=\"data row18 col4\" >-0.0145</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow18_col5\" class=\"data row18 col5\" >0.0315</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow18_col6\" class=\"data row18 col6\" >-0.3316</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow19_col0\" class=\"data row19 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow19_col1\" class=\"data row19 col1\" >0.0564</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow19_col2\" class=\"data row19 col2\" >0.007</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow19_col3\" class=\"data row19 col3\" >0.0725</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow19_col4\" class=\"data row19 col4\" >-2.5892</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow19_col5\" class=\"data row19 col5\" >0.0535</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow19_col6\" class=\"data row19 col6\" >-0.2722</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442belevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col0\" class=\"data row20 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col1\" class=\"data row20 col1\" >0.2227</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col2\" class=\"data row20 col2\" >0.1153</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col3\" class=\"data row20 col3\" >0.2702</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col4\" class=\"data row20 col4\" >-60.4467</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col5\" class=\"data row20 col5\" >0.1895</td>\n",
       "                        <td id=\"T_6bf0506c_82c0_11ea_9330_9cb6d09442berow20_col6\" class=\"data row20 col6\" >-3.6978</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15be4d7bf60>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_models(blacklist=['tr','ard'],turbo=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0151</td>\n",
       "      <td>0.8638</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.8436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0147</td>\n",
       "      <td>0.8506</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>-1.3088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0192</td>\n",
       "      <td>0.8356</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>-0.1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.8343</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>-0.2875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.9179</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.1779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0161</td>\n",
       "      <td>0.8694</td>\n",
       "      <td>0.0144</td>\n",
       "      <td>-0.6335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.8981</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.8458</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.1446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.8760</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>1.1573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.9111</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>-0.6811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.8703</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>-0.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.0288</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.6905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0110  0.0002  0.0151  0.8638  0.0127  0.8436\n",
       "1     0.0109  0.0002  0.0147  0.8506  0.0132 -1.3088\n",
       "2     0.0125  0.0004  0.0192  0.8356  0.0157 -0.1460\n",
       "3     0.0109  0.0003  0.0177  0.8343  0.0132 -0.2875\n",
       "4     0.0095  0.0002  0.0123  0.9179  0.0112  0.1779\n",
       "5     0.0109  0.0003  0.0161  0.8694  0.0144 -0.6335\n",
       "6     0.0113  0.0002  0.0155  0.8981  0.0140  0.1330\n",
       "7     0.0129  0.0004  0.0200  0.8458  0.0164  0.1446\n",
       "8     0.0105  0.0002  0.0146  0.8760  0.0126  1.1573\n",
       "9     0.0089  0.0002  0.0124  0.9111  0.0111 -0.6811\n",
       "Mean  0.0109  0.0003  0.0158  0.8703  0.0134 -0.0600\n",
       "SD    0.0011  0.0001  0.0025  0.0288  0.0016  0.6905"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_tuned = tune_model('knn',n_iter=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.8420</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.1108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>0.7930</td>\n",
       "      <td>0.0152</td>\n",
       "      <td>-1.1842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.8098</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>-0.4919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>0.8195</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>0.5177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>0.8451</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.2538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.8614</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>-0.5117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0184</td>\n",
       "      <td>0.8554</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.4126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0219</td>\n",
       "      <td>0.8158</td>\n",
       "      <td>0.0197</td>\n",
       "      <td>-0.0303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>0.8002</td>\n",
       "      <td>0.0168</td>\n",
       "      <td>0.9556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0168</td>\n",
       "      <td>0.8356</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>-0.5128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>0.8278</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>-0.0480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.5979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0123  0.0003  0.0163  0.8420  0.0153  0.1108\n",
       "1     0.0129  0.0003  0.0173  0.7930  0.0152 -1.1842\n",
       "2     0.0155  0.0004  0.0207  0.8098  0.0185 -0.4919\n",
       "3     0.0145  0.0003  0.0185  0.8195  0.0169  0.5177\n",
       "4     0.0125  0.0003  0.0169  0.8451  0.0155  0.2538\n",
       "5     0.0130  0.0003  0.0166  0.8614  0.0150 -0.5117\n",
       "6     0.0140  0.0003  0.0184  0.8554  0.0163  0.4126\n",
       "7     0.0163  0.0005  0.0219  0.8158  0.0197 -0.0303\n",
       "8     0.0140  0.0003  0.0185  0.8002  0.0168  0.9556\n",
       "9     0.0127  0.0003  0.0168  0.8356  0.0153 -0.5128\n",
       "Mean  0.0138  0.0003  0.0182  0.8278  0.0165 -0.0480\n",
       "SD    0.0013  0.0001  0.0017  0.0223  0.0015  0.5979"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "catb_tuned = tune_model('catboost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.7290</td>\n",
       "      <td>0.0198</td>\n",
       "      <td>0.0144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>0.7928</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>-0.2783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.7757</td>\n",
       "      <td>0.0204</td>\n",
       "      <td>-0.1512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0161</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0216</td>\n",
       "      <td>0.7526</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>1.5357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>0.8206</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0211</td>\n",
       "      <td>0.7765</td>\n",
       "      <td>0.0197</td>\n",
       "      <td>-0.4171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0220</td>\n",
       "      <td>0.7934</td>\n",
       "      <td>0.0203</td>\n",
       "      <td>0.1038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0187</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>0.7501</td>\n",
       "      <td>0.0227</td>\n",
       "      <td>0.0165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.7690</td>\n",
       "      <td>0.0184</td>\n",
       "      <td>0.3568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0137</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>0.8019</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>-0.5919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0152</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.7761</td>\n",
       "      <td>0.0192</td>\n",
       "      <td>0.0639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.0259</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.5553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0158  0.0005  0.0213  0.7290  0.0198  0.0144\n",
       "1     0.0126  0.0003  0.0173  0.7928  0.0159 -0.2783\n",
       "2     0.0164  0.0005  0.0224  0.7757  0.0204 -0.1512\n",
       "3     0.0161  0.0005  0.0216  0.7526  0.0202  1.5357\n",
       "4     0.0130  0.0003  0.0182  0.8206  0.0171  0.0500\n",
       "5     0.0153  0.0004  0.0211  0.7765  0.0197 -0.4171\n",
       "6     0.0159  0.0005  0.0220  0.7934  0.0203  0.1038\n",
       "7     0.0187  0.0006  0.0255  0.7501  0.0227  0.0165\n",
       "8     0.0145  0.0004  0.0199  0.7690  0.0184  0.3568\n",
       "9     0.0137  0.0003  0.0185  0.8019  0.0171 -0.5919\n",
       "Mean  0.0152  0.0004  0.0208  0.7761  0.0192  0.0639\n",
       "SD    0.0017  0.0001  0.0023  0.0259  0.0019  0.5553"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "et_tuned = tune_model('et')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>0.8245</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.2454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.8676</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>-0.3486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>0.8530</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>-0.0782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>0.8407</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.4918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>0.8958</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.1053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.8604</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>-0.4653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0183</td>\n",
       "      <td>0.8581</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>0.2026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0151</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0203</td>\n",
       "      <td>0.8412</td>\n",
       "      <td>0.0184</td>\n",
       "      <td>-0.0147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0161</td>\n",
       "      <td>0.8497</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>0.3478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0147</td>\n",
       "      <td>0.8748</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>-0.4870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.8566</td>\n",
       "      <td>0.0152</td>\n",
       "      <td>-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.0189</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.3251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0125  0.0003  0.0172  0.8245  0.0160  0.2454\n",
       "1     0.0101  0.0002  0.0138  0.8676  0.0125 -0.3486\n",
       "2     0.0135  0.0003  0.0182  0.8530  0.0163 -0.0782\n",
       "3     0.0133  0.0003  0.0174  0.8407  0.0160  0.4918\n",
       "4     0.0103  0.0002  0.0139  0.8958  0.0130  0.1053\n",
       "5     0.0124  0.0003  0.0167  0.8604  0.0153 -0.4653\n",
       "6     0.0129  0.0003  0.0183  0.8581  0.0162  0.2026\n",
       "7     0.0151  0.0004  0.0203  0.8412  0.0184 -0.0147\n",
       "8     0.0119  0.0003  0.0161  0.8497  0.0148  0.3478\n",
       "9     0.0107  0.0002  0.0147  0.8748  0.0138 -0.4870\n",
       "Mean  0.0123  0.0003  0.0166  0.8566  0.0152 -0.0001\n",
       "SD    0.0015  0.0001  0.0020  0.0189  0.0017  0.3251"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "et = create_model('et')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "961af3d368154ea583f329d5e1a18c39",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(knn_tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a714fcc11daa4e82a431874841c649d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(et)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_15c60888_82c2_11ea_b3ab_9cb6d09442berow26_col1 {\n",
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       "        }</style><table id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442be\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow0_col1\" class=\"data row0 col1\" >11</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow3_col1\" class=\"data row3 col1\" >(2379, 109)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow5_col1\" class=\"data row5 col1\" >107</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow6_col1\" class=\"data row6 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow10_col1\" class=\"data row10 col1\" >(2260, 109)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow11_col1\" class=\"data row11 col1\" >(1581, 107)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow12_col1\" class=\"data row12 col1\" >(679, 107)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow26_col1\" class=\"data row26 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow27_col1\" class=\"data row27 col1\" >0.05</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442belevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_15c60888_82c2_11ea_b3ab_9cb6d09442berow41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15be8307f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b=setup(data_22,target='Gold-T+22',\n",
    "        ignore_features=['Date'],session_id=11,\n",
    "        silent=True,profile=False,remove_outliers=True);\n",
    "        #transformation=True,\n",
    "        #pca=True,pca_method='kernel',\n",
    "        #pca_components=10,\n",
    "        #create_clusters=True,\n",
    "        #cluster_iter=10,\n",
    "        #feature_ratio=True,\n",
    "        #normalize=True,\n",
    "        #transform_target=True,\n",
    "       #silent=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>0.9179</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.9131</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>-0.4387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.8465</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>1.6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>-0.5072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.8696</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.0413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.8994</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>1.5195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.8531</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.2987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.8931</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>-0.1118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>0.8914</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.2823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0152</td>\n",
       "      <td>0.8705</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>-0.3342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.8830</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.2372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0228</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.7156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0095  0.0001  0.0122  0.9179  0.0111  0.0006\n",
       "1     0.0099  0.0002  0.0133  0.9131  0.0116 -0.4387\n",
       "2     0.0118  0.0003  0.0177  0.8465  0.0148  1.6218\n",
       "3     0.0103  0.0003  0.0158  0.8750  0.0146 -0.5072\n",
       "4     0.0107  0.0002  0.0149  0.8696  0.0135  0.0413\n",
       "5     0.0109  0.0002  0.0153  0.8994  0.0129  1.5195\n",
       "6     0.0104  0.0002  0.0150  0.8531  0.0132  0.2987\n",
       "7     0.0104  0.0002  0.0149  0.8931  0.0128 -0.1118\n",
       "8     0.0109  0.0002  0.0148  0.8914  0.0135  0.2823\n",
       "9     0.0104  0.0002  0.0152  0.8705  0.0131 -0.3342\n",
       "Mean  0.0105  0.0002  0.0149  0.8830  0.0131  0.2372\n",
       "SD    0.0006  0.0000  0.0014  0.0228  0.0011  0.7156"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_tuned = tune_model('knn',n_iter=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.8661</td>\n",
       "      <td>0.0147</td>\n",
       "      <td>0.0149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0161</td>\n",
       "      <td>0.8739</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>-0.2347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>0.8555</td>\n",
       "      <td>0.0161</td>\n",
       "      <td>0.8219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>0.8641</td>\n",
       "      <td>0.0154</td>\n",
       "      <td>-0.3188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0122</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>0.8470</td>\n",
       "      <td>0.0151</td>\n",
       "      <td>-0.1451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0197</td>\n",
       "      <td>0.8340</td>\n",
       "      <td>0.0181</td>\n",
       "      <td>1.2198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0106</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.8629</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.1561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>0.8629</td>\n",
       "      <td>0.0152</td>\n",
       "      <td>-0.6990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.8680</td>\n",
       "      <td>0.0152</td>\n",
       "      <td>0.2193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.8811</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>-0.4825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.8616</td>\n",
       "      <td>0.0152</td>\n",
       "      <td>0.0552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.5572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0119  0.0002  0.0155  0.8661  0.0147  0.0149\n",
       "1     0.0123  0.0003  0.0161  0.8739  0.0149 -0.2347\n",
       "2     0.0127  0.0003  0.0172  0.8555  0.0161  0.8219\n",
       "3     0.0116  0.0003  0.0165  0.8641  0.0154 -0.3188\n",
       "4     0.0122  0.0003  0.0162  0.8470  0.0151 -0.1451\n",
       "5     0.0143  0.0004  0.0197  0.8340  0.0181  1.2198\n",
       "6     0.0106  0.0002  0.0145  0.8629  0.0135  0.1561\n",
       "7     0.0124  0.0003  0.0169  0.8629  0.0152 -0.6990\n",
       "8     0.0123  0.0003  0.0163  0.8680  0.0152  0.2193\n",
       "9     0.0111  0.0002  0.0145  0.8811  0.0135 -0.4825\n",
       "Mean  0.0121  0.0003  0.0163  0.8616  0.0152  0.0552\n",
       "SD    0.0009  0.0000  0.0014  0.0127  0.0012  0.5572"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "et = create_model('et')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.8449</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>0.8541</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>-0.4452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0176</td>\n",
       "      <td>0.8486</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.5892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.8428</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>-0.5235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0170</td>\n",
       "      <td>0.8303</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>-0.2354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.8024</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>1.2039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0117</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.8360</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>0.3065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0184</td>\n",
       "      <td>0.8383</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>-0.4717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.8454</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>0.3077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0152</td>\n",
       "      <td>0.8701</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>-0.7064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0175</td>\n",
       "      <td>0.8413</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.0049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.5687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0130  0.0003  0.0167  0.8449  0.0155  0.0242\n",
       "1     0.0134  0.0003  0.0173  0.8541  0.0157 -0.4452\n",
       "2     0.0133  0.0003  0.0176  0.8486  0.0164  0.5892\n",
       "3     0.0125  0.0003  0.0177  0.8428  0.0166 -0.5235\n",
       "4     0.0130  0.0003  0.0170  0.8303  0.0157 -0.2354\n",
       "5     0.0155  0.0005  0.0215  0.8024  0.0199  1.2039\n",
       "6     0.0117  0.0003  0.0158  0.8360  0.0139  0.3065\n",
       "7     0.0133  0.0003  0.0184  0.8383  0.0163 -0.4717\n",
       "8     0.0136  0.0003  0.0177  0.8454  0.0162  0.3077\n",
       "9     0.0119  0.0002  0.0152  0.8701  0.0139 -0.7064\n",
       "Mean  0.0131  0.0003  0.0175  0.8413  0.0160  0.0049\n",
       "SD    0.0010  0.0001  0.0016  0.0166  0.0016  0.5687"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "catb = create_model('catboost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensembling Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.7814</td>\n",
       "      <td>0.0186</td>\n",
       "      <td>-0.0270</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0203</td>\n",
       "      <td>0.7993</td>\n",
       "      <td>0.0190</td>\n",
       "      <td>-0.1741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0210</td>\n",
       "      <td>0.7840</td>\n",
       "      <td>0.0197</td>\n",
       "      <td>1.1660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.7680</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>-0.3361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0192</td>\n",
       "      <td>0.7850</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>-0.0785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>0.7573</td>\n",
       "      <td>0.0219</td>\n",
       "      <td>0.8438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0184</td>\n",
       "      <td>0.7790</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>0.0382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.7777</td>\n",
       "      <td>0.0196</td>\n",
       "      <td>-0.7169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0210</td>\n",
       "      <td>0.7812</td>\n",
       "      <td>0.0197</td>\n",
       "      <td>0.2102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0186</td>\n",
       "      <td>0.8065</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>-0.5303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.7819</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.5520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0153  0.0004  0.0199  0.7814  0.0186 -0.0270\n",
       "1     0.0158  0.0004  0.0203  0.7993  0.0190 -0.1741\n",
       "2     0.0156  0.0004  0.0210  0.7840  0.0197  1.1660\n",
       "3     0.0153  0.0005  0.0215  0.7680  0.0202 -0.3361\n",
       "4     0.0143  0.0004  0.0192  0.7850  0.0180 -0.0785\n",
       "5     0.0171  0.0006  0.0238  0.7573  0.0219  0.8438\n",
       "6     0.0135  0.0003  0.0184  0.7790  0.0173  0.0382\n",
       "7     0.0160  0.0005  0.0215  0.7777  0.0196 -0.7169\n",
       "8     0.0155  0.0004  0.0210  0.7812  0.0197  0.2102\n",
       "9     0.0145  0.0003  0.0186  0.8065  0.0173 -0.5303\n",
       "Mean  0.0153  0.0004  0.0205  0.7819  0.0191  0.0395\n",
       "SD    0.0009  0.0001  0.0016  0.0132  0.0014  0.5520"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "et_bagged = ensemble_model(et,method='Bagging')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.9105</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>0.0624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0097</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.9211</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>-0.2473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.8762</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>1.0833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.8996</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>-0.4496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.8822</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.0291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>0.8834</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>1.3016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.8556</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.4445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.8781</td>\n",
       "      <td>0.0137</td>\n",
       "      <td>-0.4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0147</td>\n",
       "      <td>0.8938</td>\n",
       "      <td>0.0137</td>\n",
       "      <td>0.3251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>0.8470</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>1.0056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>0.8847</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0217</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.6049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0101  0.0002  0.0127  0.9105  0.0117  0.0624\n",
       "1     0.0097  0.0002  0.0127  0.9211  0.0109 -0.2473\n",
       "2     0.0113  0.0003  0.0159  0.8762  0.0133  1.0833\n",
       "3     0.0102  0.0002  0.0142  0.8996  0.0129 -0.4496\n",
       "4     0.0107  0.0002  0.0142  0.8822  0.0127  0.0291\n",
       "5     0.0120  0.0003  0.0165  0.8834  0.0143  1.3016\n",
       "6     0.0109  0.0002  0.0149  0.8556  0.0131  0.4445\n",
       "7     0.0104  0.0003  0.0159  0.8781  0.0137 -0.4300\n",
       "8     0.0111  0.0002  0.0147  0.8938  0.0137  0.3251\n",
       "9     0.0110  0.0003  0.0165  0.8470  0.0138  1.0056\n",
       "Mean  0.0107  0.0002  0.0148  0.8847  0.0130  0.3125\n",
       "SD    0.0006  0.0000  0.0013  0.0217  0.0010  0.6049"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_tuned_bagged = ensemble_model(knn_tuned, method='Bagging')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Blending Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>0.9242</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>-0.0276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.9194</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>-0.2974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.8907</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.3463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.8973</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>-0.4047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.8976</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>-0.1032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.8934</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>1.3464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0097</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.8878</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.1625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>0.9048</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>-0.4644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0137</td>\n",
       "      <td>0.9068</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.2877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.8992</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>-0.4828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0137</td>\n",
       "      <td>0.9021</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.0363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.5236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0091  0.0001  0.0117  0.9242  0.0110 -0.0276\n",
       "1     0.0099  0.0002  0.0129  0.9194  0.0114 -0.2974\n",
       "2     0.0104  0.0002  0.0149  0.8907  0.0134  0.3463\n",
       "3     0.0100  0.0002  0.0143  0.8973  0.0133 -0.4047\n",
       "4     0.0099  0.0002  0.0132  0.8976  0.0122 -0.1032\n",
       "5     0.0112  0.0002  0.0158  0.8934  0.0139  1.3464\n",
       "6     0.0097  0.0002  0.0131  0.8878  0.0120  0.1625\n",
       "7     0.0100  0.0002  0.0141  0.9048  0.0124 -0.4644\n",
       "8     0.0102  0.0002  0.0137  0.9068  0.0128  0.2877\n",
       "9     0.0096  0.0002  0.0134  0.8992  0.0120 -0.4828\n",
       "Mean  0.0100  0.0002  0.0137  0.9021  0.0124  0.0363\n",
       "SD    0.0005  0.0000  0.0011  0.0113  0.0009  0.5236"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blend_knn_et = blend_models(estimator_list=[knn_tuned,et])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.9255</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.1044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.9194</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>-0.8035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.8771</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>0.9680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0108</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.8793</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>-0.6445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.8887</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>-0.2210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>0.9063</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>1.1534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0097</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.8915</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.3532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.9012</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>-0.3576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>0.9039</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.3768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.9136</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>-0.6674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.9006</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.0262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.6497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0089  0.0001  0.0116  0.9255  0.0104  0.1044\n",
       "1     0.0096  0.0002  0.0129  0.9194  0.0115 -0.8035\n",
       "2     0.0113  0.0003  0.0158  0.8771  0.0139  0.9680\n",
       "3     0.0108  0.0002  0.0155  0.8793  0.0145 -0.6445\n",
       "4     0.0100  0.0002  0.0138  0.8887  0.0126 -0.2210\n",
       "5     0.0107  0.0002  0.0148  0.9063  0.0135  1.1534\n",
       "6     0.0097  0.0002  0.0129  0.8915  0.0116  0.3532\n",
       "7     0.0099  0.0002  0.0143  0.9012  0.0126 -0.3576\n",
       "8     0.0102  0.0002  0.0139  0.9039  0.0125  0.3768\n",
       "9     0.0092  0.0002  0.0124  0.9136  0.0110 -0.6674\n",
       "Mean  0.0100  0.0002  0.0138  0.9006  0.0124  0.0262\n",
       "SD    0.0007  0.0000  0.0013  0.0156  0.0012  0.6497"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack1 = create_stacknet(estimator_list=[[catb,knn_tuned],[et,blend_knn_et]],restack=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.9336</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>-0.0193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.9295</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>-0.6780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.9011</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.4720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0144</td>\n",
       "      <td>0.8966</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>-0.8603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.8922</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>-0.1852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.9099</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>1.1676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>0.9023</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.9115</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>-0.4792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.9173</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.2953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.9257</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>-1.0905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.9120</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>-0.1256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.6468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0085  0.0001  0.0109  0.9336  0.0101 -0.0193\n",
       "1     0.0089  0.0001  0.0120  0.9295  0.0108 -0.6780\n",
       "2     0.0100  0.0002  0.0142  0.9011  0.0130  0.4720\n",
       "3     0.0105  0.0002  0.0144  0.8966  0.0135 -0.8603\n",
       "4     0.0099  0.0002  0.0136  0.8922  0.0125 -0.1852\n",
       "5     0.0105  0.0002  0.0145  0.9099  0.0130  1.1676\n",
       "6     0.0092  0.0001  0.0122  0.9023  0.0111  0.1212\n",
       "7     0.0093  0.0002  0.0136  0.9115  0.0121 -0.4792\n",
       "8     0.0093  0.0002  0.0129  0.9173  0.0118  0.2953\n",
       "9     0.0088  0.0001  0.0115  0.9257  0.0104 -1.0905\n",
       "Mean  0.0095  0.0002  0.0130  0.9120  0.0118 -0.1256\n",
       "SD    0.0007  0.0000  0.0012  0.0136  0.0011  0.6468"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack2 = create_stacknet(estimator_list=[[catb,et,knn_tuned],[blend_knn_et]], restack=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>0.9351</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>-0.0613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>0.9270</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>-0.4676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.8908</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.8496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0147</td>\n",
       "      <td>0.8916</td>\n",
       "      <td>0.0137</td>\n",
       "      <td>-0.5214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.8942</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>-0.1646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.9220</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>1.4394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.8878</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.2767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>0.9048</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>-0.4634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.9211</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.3070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.9064</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>-0.4695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.9081</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.0725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.6220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0085  0.0001  0.0108  0.9351  0.0101 -0.0613\n",
       "1     0.0090  0.0001  0.0122  0.9270  0.0105 -0.4676\n",
       "2     0.0103  0.0002  0.0149  0.8908  0.0132  0.8496\n",
       "3     0.0098  0.0002  0.0147  0.8916  0.0137 -0.5214\n",
       "4     0.0099  0.0002  0.0135  0.8942  0.0122 -0.1646\n",
       "5     0.0099  0.0002  0.0135  0.9220  0.0119  1.4394\n",
       "6     0.0094  0.0002  0.0131  0.8878  0.0118  0.2767\n",
       "7     0.0099  0.0002  0.0141  0.9048  0.0124 -0.4634\n",
       "8     0.0096  0.0002  0.0126  0.9211  0.0118  0.3070\n",
       "9     0.0091  0.0002  0.0129  0.9064  0.0114 -0.4695\n",
       "Mean  0.0095  0.0002  0.0132  0.9081  0.0119  0.0725\n",
       "SD    0.0005  0.0000  0.0012  0.0162  0.0010  0.6220"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack3 = create_stacknet(estimator_list=[[catb,et,knn_tuned],[blend_knn_et]], restack=True,meta_model=blend_knn_et)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(model=stack2, model_name='22Day Regressor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14 Day Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Gold-T-1</th>\n",
       "      <th>Silver-T-1</th>\n",
       "      <th>Crude Oil-T-1</th>\n",
       "      <th>S&amp;P500-T-1</th>\n",
       "      <th>Russel 2000 Index-T-1</th>\n",
       "      <th>10 Yr US T-Note futures-T-1</th>\n",
       "      <th>2 Yr US T-Note Futures-T-1</th>\n",
       "      <th>Platinum-T-1</th>\n",
       "      <th>Copper-T-1</th>\n",
       "      <th>...</th>\n",
       "      <th>S&amp;P500-T-250</th>\n",
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       "      <th>Gold/15SMA</th>\n",
       "      <th>Gold/30SMA</th>\n",
       "      <th>Gold/60SMA</th>\n",
       "      <th>Gold/90SMA</th>\n",
       "      <th>Gold/180SMA</th>\n",
       "      <th>Gold/90EMA</th>\n",
       "      <th>Gold/180EMA</th>\n",
       "      <th>Gold-T+14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-12-17</td>\n",
       "      <td>0.005984</td>\n",
       "      <td>0.010417</td>\n",
       "      <td>0.003421</td>\n",
       "      <td>0.000805</td>\n",
       "      <td>0.003734</td>\n",
       "      <td>0.008306</td>\n",
       "      <td>0.000913</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>0.02439</td>\n",
       "      <td>...</td>\n",
       "      <td>0.097882</td>\n",
       "      <td>0.092219</td>\n",
       "      <td>-0.008206</td>\n",
       "      <td>-0.001805</td>\n",
       "      <td>0.012638</td>\n",
       "      <td>0.040506</td>\n",
       "      <td>0.092005</td>\n",
       "      <td>0.035494</td>\n",
       "      <td>0.070472</td>\n",
       "      <td>0.005250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-12-20</td>\n",
       "      <td>0.005005</td>\n",
       "      <td>0.006873</td>\n",
       "      <td>0.009091</td>\n",
       "      <td>0.002573</td>\n",
       "      <td>0.003592</td>\n",
       "      <td>-0.000824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007183</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100706</td>\n",
       "      <td>0.086455</td>\n",
       "      <td>-0.004174</td>\n",
       "      <td>0.003610</td>\n",
       "      <td>0.016601</td>\n",
       "      <td>0.044304</td>\n",
       "      <td>0.096390</td>\n",
       "      <td>0.039744</td>\n",
       "      <td>0.074871</td>\n",
       "      <td>0.012422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-12-21</td>\n",
       "      <td>0.001949</td>\n",
       "      <td>0.003413</td>\n",
       "      <td>0.011261</td>\n",
       "      <td>0.006014</td>\n",
       "      <td>0.010482</td>\n",
       "      <td>-0.000824</td>\n",
       "      <td>-0.000912</td>\n",
       "      <td>0.006547</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103916</td>\n",
       "      <td>0.097421</td>\n",
       "      <td>-0.002386</td>\n",
       "      <td>0.006090</td>\n",
       "      <td>0.017567</td>\n",
       "      <td>0.044925</td>\n",
       "      <td>0.097391</td>\n",
       "      <td>0.040811</td>\n",
       "      <td>0.075992</td>\n",
       "      <td>0.010555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-12-22</td>\n",
       "      <td>-0.001009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007795</td>\n",
       "      <td>0.003348</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>-0.009901</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005227</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107027</td>\n",
       "      <td>0.097143</td>\n",
       "      <td>-0.003368</td>\n",
       "      <td>0.005374</td>\n",
       "      <td>0.015569</td>\n",
       "      <td>0.042501</td>\n",
       "      <td>0.095189</td>\n",
       "      <td>0.038850</td>\n",
       "      <td>0.073962</td>\n",
       "      <td>0.002023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-12-23</td>\n",
       "      <td>-0.004903</td>\n",
       "      <td>-0.003401</td>\n",
       "      <td>0.011050</td>\n",
       "      <td>-0.001589</td>\n",
       "      <td>-0.002024</td>\n",
       "      <td>-0.003333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.004506</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100815</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>-0.007851</td>\n",
       "      <td>0.001003</td>\n",
       "      <td>0.009699</td>\n",
       "      <td>0.036124</td>\n",
       "      <td>0.088766</td>\n",
       "      <td>0.032987</td>\n",
       "      <td>0.067834</td>\n",
       "      <td>-0.004113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 109 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Gold-T-1  Silver-T-1  Crude Oil-T-1  S&P500-T-1  \\\n",
       "0 2010-12-17  0.005984    0.010417       0.003421    0.000805   \n",
       "1 2010-12-20  0.005005    0.006873       0.009091    0.002573   \n",
       "2 2010-12-21  0.001949    0.003413       0.011261    0.006014   \n",
       "3 2010-12-22 -0.001009    0.000000       0.007795    0.003348   \n",
       "4 2010-12-23 -0.004903   -0.003401       0.011050   -0.001589   \n",
       "\n",
       "   Russel 2000 Index-T-1  10 Yr US T-Note futures-T-1  \\\n",
       "0               0.003734                     0.008306   \n",
       "1               0.003592                    -0.000824   \n",
       "2               0.010482                    -0.000824   \n",
       "3               0.000127                    -0.009901   \n",
       "4              -0.002024                    -0.003333   \n",
       "\n",
       "   2 Yr US T-Note Futures-T-1  Platinum-T-1  Copper-T-1  ...  S&P500-T-250  \\\n",
       "0                    0.000913      0.001415     0.02439  ...      0.097882   \n",
       "1                    0.000000      0.007183     0.00000  ...      0.100706   \n",
       "2                   -0.000912      0.006547     0.02381  ...      0.103916   \n",
       "3                    0.000000      0.005227     0.00000  ...      0.107027   \n",
       "4                    0.000000     -0.004506     0.00000  ...      0.100815   \n",
       "\n",
       "   MSCI EM ETF-T-250  Gold/15SMA  Gold/30SMA  Gold/60SMA  Gold/90SMA  \\\n",
       "0           0.092219   -0.008206   -0.001805    0.012638    0.040506   \n",
       "1           0.086455   -0.004174    0.003610    0.016601    0.044304   \n",
       "2           0.097421   -0.002386    0.006090    0.017567    0.044925   \n",
       "3           0.097143   -0.003368    0.005374    0.015569    0.042501   \n",
       "4           0.103448   -0.007851    0.001003    0.009699    0.036124   \n",
       "\n",
       "   Gold/180SMA  Gold/90EMA  Gold/180EMA  Gold-T+14  \n",
       "0     0.092005    0.035494     0.070472   0.005250  \n",
       "1     0.096390    0.039744     0.074871   0.012422  \n",
       "2     0.097391    0.040811     0.075992   0.010555  \n",
       "3     0.095189    0.038850     0.073962   0.002023  \n",
       "4     0.088766    0.032987     0.067834  -0.004113  \n",
       "\n",
       "[5 rows x 109 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_14= data.drop(['Gold-T+22'],axis=1)\n",
    "data_14.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_b7301012_82c4_11ea_8bea_9cb6d09442berow26_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442be\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow0_col1\" class=\"data row0 col1\" >11</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow3_col1\" class=\"data row3 col1\" >(2379, 109)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow5_col1\" class=\"data row5 col1\" >107</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow6_col1\" class=\"data row6 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow10_col1\" class=\"data row10 col1\" >(2260, 109)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow11_col1\" class=\"data row11 col1\" >(1581, 107)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow12_col1\" class=\"data row12 col1\" >(679, 107)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow26_col1\" class=\"data row26 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow27_col1\" class=\"data row27 col1\" >0.05</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442belevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_b7301012_82c4_11ea_8bea_9cb6d09442berow41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15be75f0ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c=setup(data_14,target='Gold-T+14',\n",
    "        ignore_features=['Date'],session_id=11,\n",
    "        silent=True,profile=False,remove_outliers=True);\n",
    "        #transformation=True,\n",
    "        #pca=True,pca_method='kernel',\n",
    "        #pca_components=10,\n",
    "        #create_clusters=True,\n",
    "        #cluster_iter=10,\n",
    "        #feature_ratio=True,\n",
    "        #normalize=True,\n",
    "        #transform_target=True,\n",
    "       #silent=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
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       "            : ;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
       "        }    #T_7ac75938_82c5_11ea_8c23_9cb6d09442berow19_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_7ac75938_82c5_11ea_8c23_9cb6d09442berow19_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
       "        }    #T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col6 {\n",
       "            background-color:  yellow;\n",
       "            text-align:  left;\n",
       "        }</style><table id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442be\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow0_col0\" class=\"data row0 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow0_col1\" class=\"data row0 col1\" >0.0117</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow0_col2\" class=\"data row0 col2\" >0.0003</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow0_col3\" class=\"data row0 col3\" >0.0158</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow0_col4\" class=\"data row0 col4\" >0.8017</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow0_col5\" class=\"data row0 col5\" >0.0147</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow0_col6\" class=\"data row0 col6\" >0.0074</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow1_col0\" class=\"data row1 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow1_col1\" class=\"data row1 col1\" >0.0122</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow1_col2\" class=\"data row1 col2\" >0.0003</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow1_col3\" class=\"data row1 col3\" >0.0165</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow1_col4\" class=\"data row1 col4\" >0.7834</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow1_col5\" class=\"data row1 col5\" >0.0151</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow1_col6\" class=\"data row1 col6\" >-0.0629</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow2_col0\" class=\"data row2 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow2_col1\" class=\"data row2 col1\" >0.0124</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow2_col2\" class=\"data row2 col2\" >0.0003</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow2_col3\" class=\"data row2 col3\" >0.0172</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow2_col4\" class=\"data row2 col4\" >0.7615</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow2_col5\" class=\"data row2 col5\" >0.0147</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow2_col6\" class=\"data row2 col6\" >-0.1314</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow3_col1\" class=\"data row3 col1\" >0.0127</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow3_col2\" class=\"data row3 col2\" >0.0003</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow3_col3\" class=\"data row3 col3\" >0.0173</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow3_col4\" class=\"data row3 col4\" >0.7602</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow3_col5\" class=\"data row3 col5\" >0.0154</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow3_col6\" class=\"data row3 col6\" >-0.0754</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow4_col0\" class=\"data row4 col0\" >Random Forest</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow4_col1\" class=\"data row4 col1\" >0.0143</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow4_col2\" class=\"data row4 col2\" >0.0004</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow4_col3\" class=\"data row4 col3\" >0.0196</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow4_col4\" class=\"data row4 col4\" >0.695</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow4_col5\" class=\"data row4 col5\" >0.0177</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow4_col6\" class=\"data row4 col6\" >-0.0375</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow5_col0\" class=\"data row5 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow5_col1\" class=\"data row5 col1\" >0.0175</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow5_col2\" class=\"data row5 col2\" >0.0005</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow5_col3\" class=\"data row5 col3\" >0.0231</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow5_col4\" class=\"data row5 col4\" >0.5782</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow5_col5\" class=\"data row5 col5\" >0.0208</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow5_col6\" class=\"data row5 col6\" >-0.0719</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow6_col0\" class=\"data row6 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow6_col1\" class=\"data row6 col1\" >0.018</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow6_col2\" class=\"data row6 col2\" >0.0006</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow6_col3\" class=\"data row6 col3\" >0.0239</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow6_col4\" class=\"data row6 col4\" >0.5472</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow6_col5\" class=\"data row6 col5\" >0.0215</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow6_col6\" class=\"data row6 col6\" >-0.0641</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow7_col0\" class=\"data row7 col0\" >Decision Tree</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow7_col1\" class=\"data row7 col1\" >0.0199</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow7_col2\" class=\"data row7 col2\" >0.0009</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow7_col3\" class=\"data row7 col3\" >0.0298</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow7_col4\" class=\"data row7 col4\" >0.2763</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow7_col5\" class=\"data row7 col5\" >0.0204</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow7_col6\" class=\"data row7 col6\" >-0.0721</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow8_col0\" class=\"data row8 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow8_col1\" class=\"data row8 col1\" >0.0242</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow8_col2\" class=\"data row8 col2\" >0.0009</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow8_col3\" class=\"data row8 col3\" >0.0307</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow8_col4\" class=\"data row8 col4\" >0.2517</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow8_col5\" class=\"data row8 col5\" >0.0284</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow8_col6\" class=\"data row8 col6\" >0.0746</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow9_col0\" class=\"data row9 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow9_col1\" class=\"data row9 col1\" >0.0251</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow9_col2\" class=\"data row9 col2\" >0.0011</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow9_col3\" class=\"data row9 col3\" >0.0336</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow9_col4\" class=\"data row9 col4\" >0.1075</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow9_col5\" class=\"data row9 col5\" >0.028</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow9_col6\" class=\"data row9 col6\" >-0.0262</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow10_col0\" class=\"data row10 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow10_col1\" class=\"data row10 col1\" >0.0255</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow10_col2\" class=\"data row10 col2\" >0.0012</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow10_col3\" class=\"data row10 col3\" >0.0339</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow10_col4\" class=\"data row10 col4\" >0.0934</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow10_col5\" class=\"data row10 col5\" >0.0262</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow10_col6\" class=\"data row10 col6\" >-0.0265</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow11_col0\" class=\"data row11 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow11_col1\" class=\"data row11 col1\" >0.0253</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow11_col2\" class=\"data row11 col2\" >0.0012</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow11_col3\" class=\"data row11 col3\" >0.0339</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow11_col4\" class=\"data row11 col4\" >0.092</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow11_col5\" class=\"data row11 col5\" >0.0287</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow11_col6\" class=\"data row11 col6\" >-0.0319</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow12_col0\" class=\"data row12 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow12_col1\" class=\"data row12 col1\" >0.0246</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow12_col2\" class=\"data row12 col2\" >0.0012</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow12_col3\" class=\"data row12 col3\" >0.0339</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow12_col4\" class=\"data row12 col4\" >0.0919</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow12_col5\" class=\"data row12 col5\" >0.0251</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow12_col6\" class=\"data row12 col6\" >-0.0434</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow13_col0\" class=\"data row13 col0\" >Random Sample Consensus</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow13_col1\" class=\"data row13 col1\" >0.0258</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow13_col2\" class=\"data row13 col2\" >0.0012</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow13_col3\" class=\"data row13 col3\" >0.0351</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow13_col4\" class=\"data row13 col4\" >0.0293</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow13_col5\" class=\"data row13 col5\" >0.0257</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow13_col6\" class=\"data row13 col6\" >-0.0109</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow14_col0\" class=\"data row14 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow14_col1\" class=\"data row14 col1\" >0.0262</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow14_col2\" class=\"data row14 col2\" >0.0012</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow14_col3\" class=\"data row14 col3\" >0.0353</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow14_col4\" class=\"data row14 col4\" >0.0191</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow14_col5\" class=\"data row14 col5\" >0.0286</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow14_col6\" class=\"data row14 col6\" >-0.0389</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow15_col0\" class=\"data row15 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow15_col1\" class=\"data row15 col1\" >0.0271</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow15_col2\" class=\"data row15 col2\" >0.0013</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow15_col3\" class=\"data row15 col3\" >0.0358</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow15_col4\" class=\"data row15 col4\" >-0.0126</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow15_col5\" class=\"data row15 col5\" >0.0342</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow15_col6\" class=\"data row15 col6\" >-0.0918</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow16_col0\" class=\"data row16 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow16_col1\" class=\"data row16 col1\" >0.0271</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow16_col2\" class=\"data row16 col2\" >0.0013</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow16_col3\" class=\"data row16 col3\" >0.0358</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow16_col4\" class=\"data row16 col4\" >-0.0126</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow16_col5\" class=\"data row16 col5\" >0.0342</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow16_col6\" class=\"data row16 col6\" >-0.0918</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow17_col0\" class=\"data row17 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow17_col1\" class=\"data row17 col1\" >0.0271</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow17_col2\" class=\"data row17 col2\" >0.0013</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow17_col3\" class=\"data row17 col3\" >0.0358</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow17_col4\" class=\"data row17 col4\" >-0.0126</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow17_col5\" class=\"data row17 col5\" >0.0342</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow17_col6\" class=\"data row17 col6\" >-0.0918</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow18_col0\" class=\"data row18 col0\" >Support Vector Machine</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow18_col1\" class=\"data row18 col1\" >0.0353</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow18_col2\" class=\"data row18 col2\" >0.0019</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow18_col3\" class=\"data row18 col3\" >0.0431</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow18_col4\" class=\"data row18 col4\" >-0.4856</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow18_col5\" class=\"data row18 col5\" >0.025</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow18_col6\" class=\"data row18 col6\" >-0.8259</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow19_col0\" class=\"data row19 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow19_col1\" class=\"data row19 col1\" >0.0887</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow19_col2\" class=\"data row19 col2\" >0.0275</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow19_col3\" class=\"data row19 col3\" >0.1053</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow19_col4\" class=\"data row19 col4\" >-22.3359</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow19_col5\" class=\"data row19 col5\" >0.0752</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow19_col6\" class=\"data row19 col6\" >-0.1785</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442belevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col0\" class=\"data row20 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col1\" class=\"data row20 col1\" >1.663</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col2\" class=\"data row20 col2\" >32.9865</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col3\" class=\"data row20 col3\" >2.0319</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col4\" class=\"data row20 col4\" >-30551.4</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col5\" class=\"data row20 col5\" >0.3909</td>\n",
       "                        <td id=\"T_7ac75938_82c5_11ea_8c23_9cb6d09442berow20_col6\" class=\"data row20 col6\" >-125.727</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15be4c84b70>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_models(blacklist=['tr','ard'],turbo=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.8708</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>-0.1175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.8786</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.1219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.8984</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>-0.2084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.8557</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.1131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.8578</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>-1.8463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.8409</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>-0.1782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.8419</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.8853</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>-0.5482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.8784</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.1812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.8648</td>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.0160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.8673</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>-0.2395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.5736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0099  0.0002  0.0127  0.8708  0.0116 -0.1175\n",
       "1     0.0095  0.0002  0.0131  0.8786  0.0121  0.1219\n",
       "2     0.0092  0.0001  0.0120  0.8984  0.0114 -0.2084\n",
       "3     0.0094  0.0002  0.0133  0.8557  0.0110  0.1131\n",
       "4     0.0096  0.0002  0.0124  0.8578  0.0112 -1.8463\n",
       "5     0.0111  0.0002  0.0155  0.8409  0.0138 -0.1782\n",
       "6     0.0098  0.0002  0.0127  0.8419  0.0118  0.0718\n",
       "7     0.0089  0.0001  0.0120  0.8853  0.0110 -0.5482\n",
       "8     0.0089  0.0001  0.0118  0.8784  0.0107  0.1812\n",
       "9     0.0098  0.0002  0.0136  0.8648  0.0123  0.0160\n",
       "Mean  0.0096  0.0002  0.0129  0.8673  0.0117 -0.2395\n",
       "SD    0.0006  0.0000  0.0010  0.0177  0.0008  0.5736"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_tuned = tune_model('knn',n_iter=150)\n",
    "catb = create_model('catboost')\n",
    "et = create_model('et')\n",
    "knn_tuned_bagged = ensemble_model(knn_tuned, method='Bagging')\n",
    "blend_knn_et = blend_models(estimator_list=[knn_tuned,et])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>0.8816</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>-0.2419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.8909</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>-0.0291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>0.9178</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>-0.2251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.8731</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.3260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.8493</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>-1.6658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.8311</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>-0.1761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.8571</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.8889</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>-0.2962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.8466</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.2207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0097</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>-0.1283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.8706</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>-0.2150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0242</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.5213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0092  0.0001  0.0122  0.8816  0.0109 -0.2419\n",
       "1     0.0090  0.0002  0.0124  0.8909  0.0111 -0.0291\n",
       "2     0.0084  0.0001  0.0108  0.9178  0.0102 -0.2251\n",
       "3     0.0086  0.0002  0.0125  0.8731  0.0091  0.3260\n",
       "4     0.0095  0.0002  0.0128  0.8493  0.0107 -1.6658\n",
       "5     0.0112  0.0003  0.0160  0.8311  0.0139 -0.1761\n",
       "6     0.0092  0.0001  0.0121  0.8571  0.0110  0.0658\n",
       "7     0.0085  0.0001  0.0118  0.8889  0.0107 -0.2962\n",
       "8     0.0095  0.0002  0.0133  0.8466  0.0116  0.2207\n",
       "9     0.0097  0.0002  0.0134  0.8700  0.0120 -0.1283\n",
       "Mean  0.0093  0.0002  0.0127  0.8706  0.0111 -0.2150\n",
       "SD    0.0008  0.0000  0.0013  0.0242  0.0012  0.5213"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack1 = create_stacknet(estimator_list=[[catb,knn_tuned],[et,blend_knn_et]],restack=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>-0.1157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.9005</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.1077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0083</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.9241</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>-0.1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.8725</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.3352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.8649</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>-1.4615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.8340</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>-0.1595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0087</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>0.8728</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>0.8916</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>-0.2418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.8636</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.1652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.8861</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>-0.1404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.8798</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>-0.1559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0231</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.4664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0092  0.0001  0.0119  0.8875  0.0107 -0.1157\n",
       "1     0.0088  0.0001  0.0118  0.9005  0.0107  0.1077\n",
       "2     0.0083  0.0001  0.0104  0.9241  0.0097 -0.1060\n",
       "3     0.0086  0.0002  0.0125  0.8725  0.0093  0.3352\n",
       "4     0.0094  0.0001  0.0121  0.8649  0.0109 -1.4615\n",
       "5     0.0111  0.0003  0.0159  0.8340  0.0138 -0.1595\n",
       "6     0.0087  0.0001  0.0114  0.8728  0.0103  0.0573\n",
       "7     0.0084  0.0001  0.0117  0.8916  0.0107 -0.2418\n",
       "8     0.0089  0.0002  0.0125  0.8636  0.0110  0.1652\n",
       "9     0.0091  0.0002  0.0125  0.8861  0.0113 -0.1404\n",
       "Mean  0.0090  0.0002  0.0123  0.8798  0.0108 -0.1559\n",
       "SD    0.0008  0.0000  0.0013  0.0231  0.0011  0.4664"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack2 = create_stacknet(estimator_list=[[catb,et,knn_tuned],[blend_knn_et]], restack=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0087</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.8982</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>-0.1540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0087</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.9013</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>0.1448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.9113</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>-0.3816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.8686</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.2068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.8648</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>-2.5202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.8518</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>-0.2686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.8570</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.1323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0082</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>0.8975</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>-0.6362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.8596</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.2336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.8692</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.8779</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>-0.3209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.7813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
       "0     0.0087  0.0001  0.0113  0.8982  0.0101 -0.1540\n",
       "1     0.0087  0.0001  0.0118  0.9013  0.0108  0.1448\n",
       "2     0.0086  0.0001  0.0112  0.9113  0.0106 -0.3816\n",
       "3     0.0088  0.0002  0.0127  0.8686  0.0094  0.2068\n",
       "4     0.0092  0.0001  0.0121  0.8648  0.0106 -2.5202\n",
       "5     0.0107  0.0002  0.0150  0.8518  0.0131 -0.2686\n",
       "6     0.0089  0.0001  0.0121  0.8570  0.0109  0.1323\n",
       "7     0.0082  0.0001  0.0114  0.8975  0.0106 -0.6362\n",
       "8     0.0090  0.0002  0.0127  0.8596  0.0111  0.2336\n",
       "9     0.0093  0.0002  0.0134  0.8692  0.0118  0.0342\n",
       "Mean  0.0090  0.0002  0.0124  0.8779  0.0109 -0.3209\n",
       "SD    0.0006  0.0000  0.0011  0.0206  0.0009  0.7813"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack3 = create_stacknet(estimator_list=[[catb,et,knn_tuned],[blend_knn_et]], restack=True,meta_model=blend_knn_et)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(model=stack2, model_name='14Day Regressor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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